########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################
import os

import logging

logging.basicConfig(level=logging.INFO)

if __name__ == "__main__":
    from argparse import ArgumentParser
    from pytorch_lightning import Trainer
    from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
    import pytorch_lightning as pl

    rank_zero_info("########## work in progress ##########")

    parser = ArgumentParser()

    parser.add_argument("--load_model", default="", type=str)  # full path, with .pth
    parser.add_argument(
        "--wandb", default="", type=str
    )  # wandb project name. if "" then don't use wandb
    parser.add_argument("--proj_dir", default="out", type=str)
    parser.add_argument("--random_seed", default="-1", type=int)

    parser.add_argument("--data_file", default="", type=str)
    parser.add_argument("--data_type", default="utf-8", type=str)
    parser.add_argument(
        "--vocab_size", default=0, type=int
    )  # vocab_size = 0 means auto (for char-level LM and .txt data)

    parser.add_argument("--ctx_len", default=1024, type=int)
    parser.add_argument(
        "--epoch_steps", default=1000, type=int
    )  # a mini "epoch" has [epoch_steps] steps
    parser.add_argument(
        "--epoch_count", default=500, type=int
    )  # train for this many "epochs". will continue afterwards with lr = lr_final
    parser.add_argument(
        "--epoch_begin", default=0, type=int
    )  # if you load a model trained for x "epochs", set epoch_begin = x
    parser.add_argument(
        "--epoch_save", default=5, type=int
    )  # save the model every [epoch_save] "epochs"

    parser.add_argument(
        "--micro_bsz", default=12, type=int
    )  # micro batch size (batch size per GPU)
    parser.add_argument("--n_layer", default=6, type=int)
    parser.add_argument("--n_embd", default=512, type=int)
    parser.add_argument("--dim_att", default=0, type=int)
    parser.add_argument("--dim_ffn", default=0, type=int)
    parser.add_argument(
        "--pre_ffn", default=0, type=int
    )  # replace first att layer by ffn (sometimes better)
    parser.add_argument("--head_qk", default=0, type=int)  # my headQK trick
    parser.add_argument("--tiny_att_dim", default=0, type=int)  # tiny attention dim
    parser.add_argument(
        "--tiny_att_layer", default=-999, type=int
    )  # tiny attention @ which layer

    parser.add_argument(
        "--lr_init", default=6e-4, type=float
    )  # 6e-4 for L12-D768, 4e-4 for L24-D1024, 3e-4 for L24-D2048
    parser.add_argument("--lr_final", default=1e-5, type=float)
    parser.add_argument(
        "--warmup_steps", default=-1, type=int
    )  # try 50 if you load a model
    parser.add_argument("--beta1", default=0.9, type=float)
    parser.add_argument(
        "--beta2", default=0.99, type=float
    )  # use 0.999 when your model is close to convergence
    parser.add_argument("--adam_eps", default=1e-8, type=float)
    parser.add_argument(
        "--grad_cp", default=0, type=int
    )  # gradient checkpt: saves VRAM, but slower
    parser.add_argument(
        "--dropout", default=0, type=float
    )  # try 0.01 / 0.02 / 0.05 / 0.1
    parser.add_argument(
        "--weight_decay", default=0, type=float
    )  # try 0.1 / 0.01 / 0.001
    parser.add_argument("--weight_decay_final", default=-1, type=float)

    parser.add_argument(
        "--my_pile_version", default=1, type=int
    )  # my special pile version
    parser.add_argument("--my_pile_stage", default=0, type=int)  # my special pile mode
    parser.add_argument(
        "--my_pile_shift", default=-1, type=int
    )  # my special pile mode - text shift
    parser.add_argument("--my_pile_edecay", default=0, type=int)
    parser.add_argument(
        "--layerwise_lr", default=1, type=int
    )  # layerwise lr for faster convergence (but slower it/s)
    parser.add_argument(
        "--ds_bucket_mb", default=200, type=int
    )  # deepspeed bucket size in MB. 200 seems enough
    # parser.add_argument("--cuda_cleanup", default=0, type=int)  # extra cuda cleanup (sometimes helpful)

    parser.add_argument("--my_sample_len", default=0, type=int)
    parser.add_argument("--my_ffn_shift", default=1, type=int)
    parser.add_argument("--my_att_shift", default=1, type=int)
    parser.add_argument(
        "--head_size_a", default=64, type=int
    )  # can try larger values for larger models
    parser.add_argument("--head_size_divisor", default=8, type=int)
    parser.add_argument("--my_pos_emb", default=0, type=int)
    parser.add_argument("--load_partial", default=0, type=int)
    parser.add_argument("--magic_prime", default=0, type=int)
    parser.add_argument("--my_qa_mask", default=0, type=int)
    parser.add_argument("--my_random_steps", default=0, type=int)
    parser.add_argument("--my_testing", default="x052", type=str)
    parser.add_argument("--my_exit", default=99999999, type=int)
    parser.add_argument("--my_exit_tokens", default=0, type=int)

    # LORA
    parser.add_argument("--emb", action="store_true")
    parser.add_argument("--lora", action="store_true")
    parser.add_argument("--lora_load", default="", type=str)
    parser.add_argument("--lora_r", default=8, type=int)
    parser.add_argument("--lora_alpha", default=32, type=float)
    parser.add_argument("--lora_dropout", default=0.01, type=float)
    parser.add_argument("--lora_parts", default="att,ln,time", type=str)

    # LISA
    parser.add_argument("--LISA", action="store_true")
    parser.add_argument("--lisa_r", default=2, type=int)
    parser.add_argument("--lisa_k", default=100, type=int)

    # PISSA
    parser.add_argument("--PISSA", action="store_true")
    parser.add_argument("--svd_niter", default=4, type=int)

    # quant
    parser.add_argument("--quant", default="none", type=str)

    # dataset
    parser.add_argument("--dataload", default="get", type=str)

    # state tuning
    parser.add_argument("--state_tune", action="store_true")

    parser.add_argument("--chunk_ctx", default=512, type=int)
    # fla
    parser.add_argument("--fla", action="store_true")
    parser.add_argument("--train_type", default="none", type=str)

    if pl.__version__[0] == "2":
        parser.add_argument("--accelerator", default="gpu", type=str)
        parser.add_argument("--strategy", default="auto", type=str)
        parser.add_argument("--devices", default=1, type=int)
        parser.add_argument("--num_nodes", default=1, type=int)
        parser.add_argument("--precision", default="fp16", type=str)
        parser.add_argument("--accumulate_grad_batches", default=1, type=int)
    else:
        parser = Trainer.add_argparse_args(parser)
    args = parser.parse_args()

    ########################################################################################################

    import os, warnings, math, datetime, sys, time
    import numpy as np
    import torch
    from torch.utils.data import DataLoader

    if "deepspeed" in args.strategy:
        import deepspeed
    from pytorch_lightning import seed_everything

    if args.random_seed >= 0:
        print(
            f"########## WARNING: GLOBAL SEED {args.random_seed} THIS WILL AFFECT MULTIGPU SAMPLING ##########\n"
            * 3
        )
        seed_everything(args.random_seed)

    np.set_printoptions(precision=4, suppress=True, linewidth=200)
    warnings.filterwarnings(
        "ignore", ".*Consider increasing the value of the `num_workers` argument*"
    )
    warnings.filterwarnings(
        "ignore", ".*The progress bar already tracks a metric with the*"
    )
    # os.environ["WDS_SHOW_SEED"] = "1"

    args.my_timestamp = datetime.datetime.today().strftime("%Y-%m-%d-%H-%M-%S")
    args.enable_checkpointing = False
    args.replace_sampler_ddp = False
    args.logger = False
    args.gradient_clip_val = 1.0
    args.num_sanity_val_steps = 0
    args.check_val_every_n_epoch = int(1e20)
    args.log_every_n_steps = int(1e20)
    args.max_epochs = args.epoch_count  # -1 continue forever
    args.betas = (args.beta1, args.beta2)
    args.real_bsz = int(args.num_nodes) * int(args.devices) * args.micro_bsz
    os.environ["RWKV_MY_TESTING"] = args.my_testing
    os.environ["RWKV_CTXLEN"] = str(args.ctx_len)
    os.environ["RWKV_HEAD_SIZE_A"] = str(args.head_size_a)
    ######state tuning
    os.environ["RWKV_TRAIN_TYPE"] = ""
    if args.train_type == "state":
        os.environ["RWKV_TRAIN_TYPE"] = "states"
    elif args.train_type == "infctx":
        os.environ["RWKV_TRAIN_TYPE"] = "infctx"

    os.environ["WKV"] = "fla" if args.fla else ""
    if args.dim_att <= 0:
        args.dim_att = args.n_embd
    if args.dim_ffn <= 0:
        args.dim_ffn = int((args.n_embd * 3.5) // 32 * 32)  # default = 3.5x emb size

    if args.data_type == "wds_img":
        args.run_name = f"v{args.my_img_version}-{args.my_img_size}-{args.my_img_bit}bit-{args.my_img_clip}x{args.my_img_clip_scale}"
        args.proj_dir = f"{args.proj_dir}-{args.run_name}"
    else:
        args.run_name = (
            f"{args.vocab_size} ctx{args.ctx_len} L{args.n_layer} D{args.n_embd}"
        )
    if not os.path.exists(args.proj_dir):
        os.makedirs(args.proj_dir)

    if args.my_pile_stage > 0:
        magic_prime_bak = args.magic_prime

        if args.my_pile_shift < 0:
            args.my_pile_shift = 0

        if magic_prime_bak > 0:
            args.magic_prime = magic_prime_bak
        if args.my_qa_mask == 2:
            args.epoch_count = 2 * args.magic_prime // 40320
        else:
            args.epoch_count = args.magic_prime // 40320

        args.epoch_steps = 40320 // args.real_bsz
        assert args.epoch_steps * args.real_bsz == 40320
        # if args.my_pile_stage == 2:
        #     assert args.lr_final == args.lr_init
        if args.my_pile_stage >= 2:  # find latest saved model
            list_p = []
            for p in os.listdir(args.proj_dir):
                if p.startswith("rwkv") and p.endswith(".pth"):
                    p = ((p.split("-"))[1].split("."))[0]
                    if p != "final":
                        if p == "init":
                            p = -1
                        else:
                            p = int(p)
                        list_p += [p]
            list_p.sort()
            max_p = list_p[-1]
            if len(list_p) > 1:
                args.my_pile_prev_p = list_p[-2]  # in case max_p is corrupted
            if max_p == -1:
                args.load_model = f"{args.proj_dir}/rwkv-init.pth"
            else:
                args.load_model = f"{args.proj_dir}/rwkv-{max_p}.pth"
                if args.warmup_steps < 0:
                    if args.my_pile_stage == 2:
                        args.warmup_steps = 10
                    else:
                        args.warmup_steps = 30
            args.epoch_begin = max_p + 1

    samples_per_epoch = args.epoch_steps * args.real_bsz
    tokens_per_epoch = samples_per_epoch * args.ctx_len
    try:
        deepspeed_version = deepspeed.__version__
    except:
        deepspeed_version = None
        pass
    rank_zero_info(
        f"""
############################################################################
#
# RWKV-5 {args.precision.upper()} on {args.num_nodes}x{args.devices} {args.accelerator.upper()}, bsz {args.num_nodes}x{args.devices}x{args.micro_bsz}={args.real_bsz}, {args.strategy} {'with grad_cp' if args.grad_cp > 0 else ''}
#
# Data = {args.data_file} ({args.data_type}), ProjDir = {args.proj_dir}
#
# Epoch = {args.epoch_begin} to {args.epoch_begin + args.epoch_count - 1}, save every {args.epoch_save} epoch
#
# Each "epoch" = {args.epoch_steps} steps, {samples_per_epoch} samples, {tokens_per_epoch} tokens
#
# Model = {args.n_layer} n_layer, {args.n_embd} n_embd, {args.ctx_len} ctx_len
#
# Adam = lr {args.lr_init} to {args.lr_final}, warmup {args.warmup_steps} steps, beta {args.betas}, eps {args.adam_eps}
#
# Found torch {torch.__version__}, recommend 1.13.1+cu117 or newer
# Found deepspeed {deepspeed_version}, recommend 0.7.0 (faster than newer versions)
# Found pytorch_lightning {pl.__version__}, recommend 1.9.5
#
############################################################################
"""
    )
    rank_zero_info(str(vars(args)) + "\n")

    assert args.data_type in ["utf-8", "utf-16le", "numpy", "binidx", "dummy", "uint16"]

    if args.lr_final == 0 or args.lr_init == 0:
        rank_zero_info(
            "\n\nNote: lr_final = 0 or lr_init = 0. Using linear LR schedule instead.\n\n"
        )

    assert args.precision in ["fp32", "tf32", "fp16", "bf16"]
    os.environ["RWKV_FLOAT_MODE"] = args.precision
    if args.precision == "fp32":
        for i in range(10):
            rank_zero_info(
                "\n\nNote: you are using fp32 (very slow). Try bf16 / tf32 for faster training.\n\n"
            )
    if args.precision == "fp16":
        rank_zero_info(
            "\n\nNote: you are using fp16 (might overflow). Try bf16 / tf32 for stable training.\n\n"
        )

    os.environ["RWKV_JIT_ON"] = "0"
    if "deepspeed_stage_3" in args.strategy:
        os.environ["RWKV_JIT_ON"] = "0"

    torch.backends.cudnn.benchmark = True
    torch.backends.cudnn.enabled = True
    if args.precision == "fp32":
        torch.backends.cudnn.allow_tf32 = False
        torch.backends.cuda.matmul.allow_tf32 = False
    else:
        torch.backends.cudnn.allow_tf32 = True
        torch.backends.cuda.matmul.allow_tf32 = True

    if "32" in args.precision:
        args.precision = 32
    elif args.precision == "fp16":
        args.precision = 16
    else:
        args.precision = "bf16"

    ########################################################################################################

    from src.trainer import train_callback, generate_init_weight
    from src.dataset import MyDataset

    train_data = MyDataset(args)
    args.vocab_size = train_data.vocab_size

    from src.model import RWKV, LORA_CONFIG, LoraLinear

    if args.lora:
        assert args.lora_r > 0, "LoRA should have its `r` > 0"
        LORA_CONFIG["r"] = args.lora_r
        LORA_CONFIG["alpha"] = args.lora_alpha
        LORA_CONFIG["dropout"] = args.lora_dropout
        LORA_CONFIG["parts"] = set(str(args.lora_parts).split(","))
        enable_time_finetune = "time" in LORA_CONFIG["parts"]
        enable_ln_finetune = "ln" in LORA_CONFIG["parts"]
    model = RWKV(args)
    freeze = False
    if args.lora or args.LISA or args.train_type == "state":
        model.requires_grad_(False)
        freeze = True

    if args.state_tune or args.train_type == "state":
        for name, module in model.named_modules():
            for pname, param in module.named_parameters():
                if "state" in pname:
                    param.requires_grad = True
            break

    if args.LISA:
        import re

        select_layers = np.random.choice(
            range(args.n_layer), args.lisa_r, replace=False
        )
        for name, module in model.named_modules():
            for pname, param in module.named_parameters():
                if (
                    "emb" in pname
                    or "head" in pname
                    or ".ln" in pname
                    or "time" in pname
                ):
                    param.requires_grad = True
                match = re.search(r"\d+", pname)
                if match:
                    number = int(match.group())
                    if number in select_layers:
                        param.requires_grad = True
            break

    elif args.lora:

        for name, module in model.named_modules():
            if len(args.load_model) == 0:
                if any(n.startswith("emb.") for n, _ in module.named_parameters()):
                    for pname, param in module.named_parameters():
                        if "emb.weight" == pname:
                            print(f"  EMB additionally training module {pname}")
                            param.requires_grad = True
                if any(n.startswith("head.") for n, _ in module.named_parameters()):
                    for pname, param in module.named_parameters():
                        if "head.weight" == pname:
                            print(f"  head additionally training module {pname}")
                            param.requires_grad = True
                if "ln" in name:
                    print(f"  LoRA additionally training module {name}")
                    for param in module.parameters():
                        param.requires_grad = True
            if any(n.startswith("emb.") for n, _ in module.named_parameters()):
                for pname, param in module.named_parameters():
                    if args.emb and "emb.weight" == pname:
                        print(f"  EMB additionally training module {pname}")
                        param.requires_grad = True
            if any(n.startswith("head.") for n, _ in module.named_parameters()):
                for pname, param in module.named_parameters():
                    if args.emb and "head.weight" == pname:
                        print(f"  head additionally training module {pname}")
                        param.requires_grad = True
            if any(n.startswith("lora_") for n, _ in module.named_parameters()):
                print(f"  LoRA additionally training module {name}")
                for pname, param in module.named_parameters():
                    param.requires_grad = "lora_" in pname
            elif enable_ln_finetune and ".ln" in name:
                print(f"  LoRA additionally training module {name}")
                for param in module.parameters():
                    param.requires_grad = True
            elif enable_time_finetune and any(
                n.startswith("time") for n, _ in module.named_parameters()
            ):
                for pname, param in module.named_parameters():
                    if pname.startswith("time"):
                        print(f"  LoRA additionally training parameter {pname}")
                        param.requires_grad = True

    if (
        len(args.load_model) == 0 or args.my_pile_stage == 1
    ):  # shall we build the initial weights?
        init_weight_name = f"{args.proj_dir}/rwkv-init.pth"
        generate_init_weight(model, init_weight_name)  # save initial weights
        args.load_model = init_weight_name

    rank_zero_info(f"########## Loading {args.load_model}... ##########")
    try:
        load_dict = torch.load(args.load_model, map_location="cpu")
        load_keys = list(load_dict.keys())
        for k in load_keys:
            if k.startswith("_forward_module."):
                load_dict[k.replace("_forward_module.", "")] = load_dict[k]
                del load_dict[k]
    except:
        rank_zero_info(f"Bad checkpoint {args.load_model}")
        if args.my_pile_stage >= 2:  # try again using another checkpoint
            max_p = args.my_pile_prev_p
            if max_p == -1:
                args.load_model = f"{args.proj_dir}/rwkv-init.pth"
            else:
                args.load_model = f"{args.proj_dir}/rwkv-{max_p}.pth"
            args.epoch_begin = max_p + 1
            rank_zero_info(f"Trying {args.load_model}")
            load_dict = torch.load(args.load_model, map_location="cpu")

    if args.load_partial == 1:
        load_keys = load_dict.keys()
        for k in model.state_dict():
            if k not in load_keys:
                load_dict[k] = model.state_dict()[k]
    model.load_state_dict(load_dict, strict=(not freeze))
    if os.path.isfile(args.lora_load):
        model.load_state_dict(
            torch.load(args.lora_load, map_location="cpu"), strict=False
        )
    if args.PISSA:
        init_dict = {}
        rank_zero_info(f"########## Init PISSA... ##########")
        for name, m in model.named_modules():
            if hasattr(m, "pissa_init") and callable(getattr(m, "pissa_init")):
                m.pissa_init(args.svd_niter)
                init_dict[f"{name}.init_lora_A"] = m.lora_A.data
                init_dict[f"{name}.init_lora_B"] = m.lora_B.data
        torch.save(init_dict, f"{args.proj_dir}/init_lora.pth")

    if args.quant != "none":
        rank_zero_info(f"########## Quant... ##########")
        for name, m in model.named_modules():
            if hasattr(m, "quant") and callable(getattr(m, "quant")):
                m.quant(args.quant)

    if pl.__version__[0] == "2":
        trainer = Trainer(
            accelerator=args.accelerator,
            strategy=args.strategy,
            devices=args.devices,
            num_nodes=args.num_nodes,
            precision=args.precision,
            logger=args.logger,
            callbacks=[train_callback(args)],
            max_epochs=args.max_epochs,
            check_val_every_n_epoch=args.check_val_every_n_epoch,
            num_sanity_val_steps=args.num_sanity_val_steps,
            log_every_n_steps=args.log_every_n_steps,
            enable_checkpointing=args.enable_checkpointing,
            accumulate_grad_batches=args.accumulate_grad_batches,
            gradient_clip_val=args.gradient_clip_val,
        )
    else:
        trainer = Trainer.from_argparse_args(
            args,
            callbacks=[train_callback(args)],
        )

    if trainer.global_rank == 0:
        for n in model.state_dict():
            shape = model.state_dict()[n].shape
            shape = [i for i in shape if i != 1]
            if len(shape) > 1:
                print(f"{str(shape[0]).ljust(5)} {str(shape[1]).ljust(5)} {n}")
            else:
                print(f"{str(shape[0]).ljust(5)}       {n}")

    if "deepspeed" in args.strategy:
        trainer.strategy.config["zero_optimization"]["allgather_bucket_size"] = (
            args.ds_bucket_mb * 1000 * 1000
        )
        trainer.strategy.config["zero_optimization"]["reduce_bucket_size"] = (
            args.ds_bucket_mb * 1000 * 1000
        )

    # must set shuffle=False, persistent_workers=False (because worker is in another thread)
    data_loader = DataLoader(
        train_data,
        shuffle=False,
        pin_memory=True,
        batch_size=args.micro_bsz,
        num_workers=1,
        persistent_workers=False,
        drop_last=True,
    )

    trainer.fit(model, data_loader)
    # if args.LISA:
    #     args.load_model=f'rwkv-0.pth'
    #     model = RWKV(args)
    #     model.requires_grad_(False)

    #     select_layers = np.random.choice(range(args.n_layer), args.lisa_r, replace=False)
    #     for name, module in model.named_modules():
    #         for pname, param in module.named_parameters():
    #             if 'emb' in pname or 'head' in pname or '.ln' in pname or 'time' in pname :
    #                 param.requires_grad = True
    #             match = re.search(r'\d+', pname)
    #             if match:
    #                 number = int(match.group())
    #                 if number in select_layers:
    #                     param.requires_grad  = True
    #         break
    #         rank_zero_info(f"########## Loading {args.load_model}... ##########")
    #     try:
    #         load_dict = torch.load(args.load_model, map_location="cpu")
    #         load_keys = list(load_dict.keys())
    #         for k in load_keys:
    #             if k.startswith('_forward_module.'):
    #                 load_dict[k.replace('_forward_module.','')] = load_dict[k]
    #                 del load_dict[k]
    #     except:
    #         rank_zero_info(f"Bad checkpoint {args.load_model}")
    #         if args.my_pile_stage >= 2:  # try again using another checkpoint
    #             max_p = args.my_pile_prev_p
    #             if max_p == -1:
    #                 args.load_model = f"{args.proj_dir}/rwkv-init.pth"
    #             else:
    #                 args.load_model = f"{args.proj_dir}/rwkv-{max_p}.pth"
    #             args.epoch_begin = max_p + 1
    #             rank_zero_info(f"Trying {args.load_model}")
    #             load_dict = torch.load(args.load_model, map_location="cpu")

    #     if args.load_partial == 1:
    #         load_keys = load_dict.keys()
    #         for k in model.state_dict():
    #             if k not in load_keys:
    #                 load_dict[k] = model.state_dict()[k]
    #     model.load_state_dict(load_dict, strict=(not args.lora))

    #     if pl.__version__[0]=='2':
    #         trainer = Trainer(accelerator=args.accelerator,strategy=args.strategy,devices=args.devices,num_nodes=args.num_nodes,precision=args.precision,
    #         logger=args.logger,callbacks=[train_callback(args)],max_epochs=args.max_epochs,check_val_every_n_epoch=args.check_val_every_n_epoch,num_sanity_val_steps=args.num_sanity_val_steps,
    #         log_every_n_steps=args.log_every_n_steps,enable_checkpointing=args.enable_checkpointing,accumulate_grad_batches=args.accumulate_grad_batches,gradient_clip_val=args.gradient_clip_val)
    #     else:
    #         trainer = Trainer.from_argparse_args(
    #             args,
    #             callbacks=[train_callback(args)],
    #         )

    #     if trainer.global_rank == 0:
    #         for n in model.state_dict():
    #             shape = model.state_dict()[n].shape
    #             shape = [i for i in shape if i != 1]
    #             if len(shape) > 1:
    #                 print(f"{str(shape[0]).ljust(5)} {str(shape[1]).ljust(5)} {n}")
    #             else:
    #                 print(f"{str(shape[0]).ljust(5)}       {n}")

    #     if "deepspeed" in args.strategy:
    #         trainer.strategy.config["zero_optimization"]["allgather_bucket_size"] = args.ds_bucket_mb * 1000 * 1000
    #         trainer.strategy.config["zero_optimization"]["reduce_bucket_size"] = args.ds_bucket_mb * 1000 * 1000

    #     # must set shuffle=False, persistent_workers=False (because worker is in another thread)
    #     data_loader = DataLoader(train_data, shuffle=False, pin_memory=True, batch_size=args.micro_bsz, num_workers=1, persistent_workers=False, drop_last=True)

    #     trainer.fit(model, data_loader)
